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Order model for dependency structure

a dependency structure and order model technology, applied in the field of order model for dependency structure and the machine translation system, can solve the problem of not being the most effective representation of syntax in machine translation, and achieve the effect of effective phrasal statistical machine translation

Inactive Publication Date: 2006-05-25
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it is believed that this is not the most effective representation for syntax in machine translation.

Method used

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  • Order model for dependency structure
  • Order model for dependency structure
  • Order model for dependency structure

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Embodiment Construction

[0040] The present invention deals with machine translation using syntactic dependency trees. However, prior to describing the present invention in greater detail, one illustrative environment in which the present invention can be used will be discussed.

[0041]FIG. 1 illustrates an example of a suitable computing system environment 100 on which the invention may be implemented. The computing system environment 100 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment 100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 100.

[0042] The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and / or configurati...

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Abstract

In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.

Description

BACKGROUND OF THE INVENTION [0001] The present invention deals with machine translation. More specifically, the present invention deals with a machine translation system that uses syntactic dependency treelets. [0002] Machine translation involves the process of receiving an input text fragment in a source language and translating it, automatically through the use of a computing device, to a corresponding text fragment in a target language. Machine translation has typically been attempted using one of two different approaches. The first is a knowledge engineered approach, typically using a linguistic parser and hand-crafted transfer rules. Almost all commercial translation systems (such as Systran) are of this type. The second is a corpus motivated approach, typically either example-based machine translation (EBMT) or statistical machine translation (SMT). However, SMT appears more promising in current research, so this discussion will focus primarily on SMT and not EBMT. Typically t...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F17/28
CPCG06F17/28G06F17/2818G06F40/40G06F40/44
Inventor MENEZES, ARUL A.QUIRK, CHRISTOPHER B.CHERRY, COLIN A.
Owner MICROSOFT TECH LICENSING LLC
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